Today, we are lucky to have Daniel Levine of RJMetrics provide a guest post. RJMetrics created an extensive report detailing The State of Data Science. I asked Daniel to provide some results as they relate to the current education of data scientists.
Recently, RJMetrics released a benchmark report that looked to answer many of the questions people have about today’s data scientists, such as how many data scientists are there, what degrees do they have, and what skills do they posses.
From LinkedIn data on the 11,400 data scientists working now, we can get a much better sense of what types of data scientists companies are hiring, and how senior data scientists differ from their junior counterparts.
While it was typical to see data scientists report multiple degrees, when we looked at the percentages of all distinct bachelor’s, master’s, and doctorate degrees, we found that 42% finished their education with a master’s.
The high number of data scientists that receive graduate degrees (79%) is indicative of the increasing demand for specialists and a desire from data scientist for advanced training.
So what does this distribution look like as you climb the corporate ladder? You may assume that the higher the position, the more PhDs; but in fact, across Junior, Senior, and Chief Data Scientists, we saw the highest ratio of PhDs to Master’s at the Senior level.
We speculate that the drop from 43% at the Senior level to 35% at the chief level actually reflects how long those individuals have been in the field. In a study by Heirick & Struggles titled, “Understanding Today’s Chief Data Scientist,” they found that chief Data Scientists “average nearly 15 years of post-degree commercial (PDC) experience.” What we’re likely seeing in this data is the “first crop” of Chief Data Scientists who earned this title in the field, not in the classroom.
When we looked at what data scientists studied during their education, we found that besides Business Administration/Management, they were mostly STEM-focused.
We believe that Computer Science is so popular because a data scientist without CS skills is at an extreme disadvantage because they won’t be able to extract the data well enough to properly analyze it. DJ Patil and Hilary Mason, in their book Creating a Data Culture, went as far as to say, “a data scientist who lacks the tools to get data from a database into an analysis package and back out again will become a second-class citizen in the technical organization.”
In analyzing 254,600 records of skills, we found the most popular skills to be more generic than we’d expect. Popular buzz term like “big data” and “hadoop” didn’t crack the top 10, while programming languages like “r” and “python” are extremely popular among data scientists.
When the data was sliced by seniority, we saw a major difference between Junior, Senior, and Chief levels. To make these differences easier to digest, we compared each level to the same common denominator: the average data scientist.
Again, the chief data scientists data is of particular interest. These C-suite professionals are more likely to list skills like “business intelligence,” “analytics,” “leadership,” “strategy,” and “management” among their skills than both junior and senior data scientists; but less likely to list skills on the more technical side, like “python” and “r”.
While it’s true that chief data scientists may be simply emphasizing skills that are more relevant to their position within the company, we also speculate that many chief data scientists assumed these roles by virtue of being in the field longer or having additional qualifications, such as a business degree. Therefore, it is also possible that some chief data scientists never actually learned many of the skills listed by more junior people.
If you’d like more analysis about this data and a more detailed explanation about our methods, you can check out the full State of Data Science.